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133 results for “conservation models”
Data from: Spatio-temporal modelling of auk abundance after the Erika oil spill and implications for conservation
Species distribution models are widely used in applied ecology and conservation. While accounting for spatial dependences is now the rule, temporal dependences have rarely been dealt with explicitly. In this study, we analyse wintering auk distribution in the Bay of Biscay and English Channel and estimate changes in abundance within and between years while accounting for space–time dependencies. We then propose a retrospective estimate of the impact of the Erika oil spill that occurred in December 1999. Two series of extensive aerial surveys, repeated at intervals of 1–2 months, were carried out at a 10-year interval off the French Atlantic coast (2001–2002 and 2011–2012). Spatially and temporally explicit Bayesian models were fitted to these data to provide spatio-temporal predictions of auk abundance. These were then used to compare abundances within the area affected by the Erika oil spill two and twelve years after the catastrophe. The results showed that 1·55 million auks wintered in the study area in 2011–2012. The main wintering area was the English Channel (more than one million auks) but the Bay of Biscay also became an important area in the middle of winter (470 000 auks) owing to a strong southward shift in auk distribution. Two years after the catastrophe (2001–2002), the area affected by the Erika oil spill hosted a small proportion of auks of the Bay of Biscay – about 80 000 individuals. This number increased by more than three times 10 years later and reached 270 000 individuals, whereas no significant change was detected elsewhere. We suggest that it could result from a recovery after the extra-mortality induced by the Erika oil spill. Policy implications. This study identified major auk wintering areas, with abundances much higher than previously realized. Oil spills have occurred regularly in these areas, with major delayed impacts on auk breeding populations. The worst case scenario would be if a major oil spill occurred in the English Channel in February, when abundance reaches one million auks. Although such a disaster has not so far occurred, stricter policies on the transport of hydrocarbons should be implemented to prevent such a possibility.
Data from: Development and validation of an expert-based habitat suitability model to support boreal caribou conservation
The long-term persistence of boreal caribou (Rangifer tarandus caribou) is threatened by the negative impacts of human activities, including industrial development. The vast geographical distribution and behavioural variability of caribou warranted the development and validation of an appropriate conservation tool for managers in eastern Canada. We developed a habitat suitability model for boreal caribou in Québec by integrating expert knowledge into a hierarchical analysis. We elicited responses from 14 experts on caribou ecology to determine the best spatiotemporal scales of application, the relative importance of habitat variables, the zone of influence of human infrastructure, and the parameterization of the model. Based on this input, we built the model using 8 habitat categories and 3 human infrastructure variables. Experts identified mature conifer-dominated forests and open lichen woodlands as the most important vegetation categories for boreal caribou, whereas density of and proximity to paved roads, forest roads, and mines decreased habitat quality. We mapped the resulting model over the entire province of Québec (up to the northern forest allocation limit), and validated it using independent GPS telemetry datasets acquired in 3 distinct regions. Our model predicted that habitat suitability was highest in the northeastern part of our study area, where timber harvest activities and roads were virtually absent. Conversely, southern parts of Québec were generally unsuitable for boreal caribou. Our habitat suitability model is among the first tools for boreal caribou conservation available to wildlife and land managers in Québec.
Data from: Evaluating conservation and fisheries management strategies by linking spatial prioritisation software and ecosystem and fisheries modelling tools
1. Well-designed marine protected area (MPA) networks can deliver a range of ecological, economic and social benefits, and so a great deal of research has focused on developing spatial conservation prioritization tools to help identify important areas. 2. However, whilst these software tools are designed to identify MPA networks that both represent biodiversity and minimize impacts on stakeholders, they do not consider complex ecological processes. Thus, it is difficult to determine the impacts that proposed MPAs could have on marine ecosystem health, fisheries and fisheries sustainability. 3. Using the eastern English Channel as a case study, this paper explores an approach to address these issues by identifying a series of MPA networks using the Marxan and Marxan with Zones conservation planning software and linking them with a spatially explicit ecosystem model developed in Ecopath with Ecosim. We then use these to investigate potential trade-offs associated with adopting different MPA management strategies. 4. Limited-take MPAs, which restrict the use of some fishing gears, could have positive benefits for conservation and fisheries in the eastern English Channel, even though they generally receive far less attention in research on MPA network design. 5. Our findings, however, also clearly indicate that no-take MPAs should form an integral component of proposed MPA networks in the eastern English Channel, as they not only result in substantial increases in ecosystem biomass, fisheries catches and the biomass of commercially valuable target species, but are fundamental to maintaining the sustainability of the fisheries. 6. Synthesis and applications. Using the existing software tools Marxan with Zones and Ecopath with Ecosim in combination provides a powerful policy-screening approach. This could help inform marine spatial planning by identifying potential conflicts and by designing new regulations that better balance conservation objectives and stakeholder interests. In addition, it highlights that appropriate combinations of no-take and limited-take marine protected areas might be the most effective when making trade-offs between long-term ecological benefits and short-term political acceptability.
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
Data from: Ecological niche modelling for conservation planning of an endemic snail in the verge of becoming a pest in cardamom plantations in the Western Ghats biodiversity hotspot
Conservation managers and policy makers are often confronted with a challenging dilemma of devising suitable strategies to maintain agricultural productivity while conserving endemic species that at the early stages of becoming pests of agricultural crops. Identification of environmental factors conducive to species range expansion for forecasting species distribution patterns will play a central role in devising management strategies to minimize the conflict between the agricultural productivity and biodiversity conservation. Here, we present results of a study that predicts the distribution of Indrella ampulla, a snail endemic to the Western Ghats biodiversity hotspot, which is becoming a pest in cardamom (Ellettaria cardamomum) plantations. We determined the distribution patterns and niche overlap between I. ampulla and Ellettaria cardamomum using maximum entropy (MaxEnt) niche modeling techniques under current and future (2020–2080) climatic scenarios. The results showed that climatic (precipitation of coldest quarter and isothermality) and soil (cation exchange capacity of soil [CEC]) parameters are major factors that determine the distribution of I. ampulla in Western Ghats. The model predicted cardamom cultivation areas in southern Western Ghats are highly sensitive to invasion of I. ampulla under both present and future climatic conditions. While the land area in the central Western Ghats is predicted to become unsuitable for I. ampulla and Ellettaria cardamomum in future, we found 71% of the Western Ghats land area is suitable for Ellettaria cardamomum cultivation and 45% suitable for I. ampulla, with an overlap of 35% between two species. The resulting distribution maps are invaluable for policy makers and conservation managers to design and implement management strategies minimizing the conflicts to sustain agricultural productivity while maintaining biodiversity in the region.
Data from: Conservation versus livelihoods: spatial management of non-timber forest product harvests in a two-dimensional model
Areas of high biodiversity often coincide with communities living in extreme poverty. As a livelihood support, these communities often harvest wild products from the environment. But harvest activities can have negative impacts on fragile and globally important ecosystems. This paper examines trade-offs in ecological protection and community welfare from the harvest of wild products. With a novel model and empirical evidence, I show that management of harvest activity does not always resolve these trade-offs. In a model of continuous harvests in a two-dimensional landscape, managed harvest activity improves welfare, but is uniformly bad for other ecosystem services that are sensitive to the presence (as opposed to the intensity) of human activity. Empirical results from a unique dataset of mushroom harvesters in Yunnan, China suggest more experienced, poorer, and more vulnerable individuals tend to rely on more distant harvests. Thus, policies that limit the extent of forest travel, such as protected areas, may protect fragile ecosystems but can have a disproportionately negative effect on those most vulnerable.
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined. in Morphological and phylogenetic relations of members of the genus Coelastrella (Scenedesmaceae, Chlorophyta) from the Ural and Khentii Mountains (Russia, Mongolia)
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined.
Using species distribution modeling to generate relative abundance information in unstable territories: conservation of Felidae in Mexico
<p>Raw data used in the abovementioned manuscript</p>
Dataset of Gunshot Sounds and Koogu Model related to "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" by Yoh et al. 2024
<h2>Description</h2> <p>This repository contains gunshot sound training data, testing data, and the Convolutional Neural Network (CNN) model used in our study on gunhunting patterns in Gabon. The dataset includes hundreds of audio recordings containing gunshots and other environmental sounds, their associated annotations, and the trained Koogu machine-learning model.</p> <h2>Data Summary</h2> <ul> <li><strong>Data Types:</strong> Gunshot sound recordings, non-gunshot sound recordings, annotations, model files. Data are in the format required for Koogu, where training and test data are contained in folders of audio files with associated folders containing the Raven Pro selection tables. </li> <li><strong>Data Format:</strong> <ul> <li>Audio files: .wav</li> <li>Annotations: .txt</li> <li>Model: TensorFlow/Keras model (.h5)</li> <li>Code: .py</li> </ul> </li> </ul> <h2>Data Details</h2> <p><em>Training and Test Data:</em></p> <ul> <li>train_annotations <ul> <li>"Training_Gunshots_Batch1.txt" is a Raven Pro selection table that contains 203 gunshot annotations that were detected through the spectral cross-correlation analysis. </li> <li>"Training_Gunshots_Batch2.txt" is a Raven Pro selection table that contains 223 gunshot annotations that were detected through the initial Koogu model that was run over the whole dataset.</li> <li>"Training_NotGunshots.txt" is a Raven Pro selection table that contains 8,614 non-gunshot annotations, including sounds from branch snaps, tree falls, calls of the putty-nosed monkey, ambient noise, etc.</li> </ul> </li> <li>train_audio <ul> <li>"audio_files" contains 184 audio files associated with the selection table "Training_Gunshots_Batch1.txt"</li> <li>"audio_files_second_batch" contains 203 clips associated with the selection table "Training_Gunshots_Batch2.txt". </li> <li>"other_clips" contains 9,274 clips associated with the selection table "Training_NotGunshots.txt".</li> </ul> </li> <li>test_annotations <ul> <li>"allgunshots_test_gunshots_adjusted_final_clean_20221120.txt" is a Raven Pro selection table that contains the 138 gunshot annotations associated with the files in the "test_audio" folder. This comprises the test dataset. </li> </ul> </li> <li>test_audio<br> <ul> <li>Contains the 131 recordings associated with the 138 gunshot annotations in "allgunshots_test_gunshots_adjusted_final_clean_20221120.txt"</li> </ul> </li> </ul> <p><em>Koogu Model:</em></p> <ul> <li>qDN_4x8_x4_16_Allsites_V3<br> <ul> <li>This is the Koogu model used in this study. It can be run using the script "Koogu_Run_Local_Python_Gabon_ModelV3.py" that you modify depending on the locations of your data and model. </li> </ul> </li> </ul> <p><em>CoLab</em>: An example Koogu CoLab page that contains the code used to train and test the model is available <a href="https://colab.research.google.com/drive/1TerOhzsCs9zSMj9uQ31HX7XPKG1XFYCt?usp=sharing">here.</a></p> <h2>Data Collection</h2> <ul> <li><strong>Collection Method:</strong> BAR-LT audio recorders deployed in 110 sites within Gabonese national parks, logging concessions, and community forests.</li> <li><strong>Time Period:</strong> Recordings from February 2021 to June 2022.</li> <li><strong>Geographical Information:</strong> Specific coordinates provided upon request. </li> </ul> <h2>Data Preparation</h2> <ul> <li><strong>Downsampling:</strong> Recordings were downsampled from 44.1 kHz to 4 kHz.</li> <li><strong>Segmentation:</strong> Audio recordings split into consecutive 2.25-second segments with 1.5 seconds of overlap.</li> <li><strong>Normalization:</strong> Waveform normalized to [-1.0, 1.0].</li> <li><strong>Spectrogram Computation:</strong> Using a 64 ms window length and 50% overlap, trimmed to 10–1200 Hz frequency range.</li> <li><strong>Training Inputs:</strong> Consisted of 584 positive class (gunshots) and 25842 negative class spectrograms.</li> </ul> <h2>Requirements</h2> <ul> <li><strong>Software Requirements:</strong> Python 3.8, TensorFlow 2.5, NumPy, Pandas.</li> <li><strong>Hardware Requirements:</strong> GPU with at least 8GB VRAM recommended.</li> </ul> <h2>References</h2> <ul> <li>For more information about this dataset and the model specifications, please consult "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" (Yoh et al. 2024)</li> </ul> <h2>How to Cite this Dataset:</h2> <ul> <li>If you use this dataset in your work, please cite it: <ul> <li>Gottesman, B. (2024). Dataset of Gunshot Sounds and Koogu Model related to "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" by Yoh et al. 2024 (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11192704" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11192704</a></li> </ul> </li> </ul>
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
<p>This dataset accompanies the GMD article 'A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0' (https://doi.org/10.5194/egusphere-2024-96).</p> <ul> <li>The input files to run the presented cases using uDALES are contained in 'inputs'.</li> <li>The model outputs are contained in separate folders: 'XCC', 'indoor-outdoor', 'XCB', and 'SEB'. When downloaded, move into a folder called 'outputs' so that the paths defined in the scripts work as intended (see below).</li> <li>The Matlab scripts to plot the figures are contained in 'scripts'.</li> <li>The figures shown in the article are contained in 'figures'.</li> </ul>
Data from: Using a full annual cycle model to evaluate long-term population viability of the conservation-reliant Kirtland's warbler after successful recovery
Long-term management planning for conservation-reliant migratory songbirds is particularly challenging because habitat quality in different stages and geographic locations of the annual cycle can have direct and carry-over effects that influence the population dynamics. The Neotropical migratory songbird Kirtland's warbler Setophaga kirtlandii (Baird 1852) is listed as endangered under the U.S. Endangered Species Act and Near Threatened under the IUCN Red List. This conservation-reliant species is being considered for U.S. federal delisting because the species has surpassed the designated 1000 breeding pairs recovery threshold since 2001. To help inform the delisting decision and long-term management efforts, we developed a population simulation model for the Kirtland's warbler that incorporated both breeding and wintering grounds habitat dynamics, and projected population viability based on current environmental conditions and potential future management scenarios. Future management scenarios included the continuation of current management conditions, reduced productivity and carrying capacity due to the changes in habitat suitability from the creation of experimental jack pine Pinus banksiana (Lamb.) plantations, and reduced productivity from alteration of the brown-headed cowbird Molothrus ater (Boddaert 1783) removal programme. Linking wintering grounds precipitation to productivity improved the accuracy of the model for replicating past observed population dynamics. Our future simulations indicate that the Kirtland's warbler population is stable under two potential future management scenarios: (i) continuation of current management practices and (ii) spatially restricting cowbird removal to the core breeding area, assuming that cowbirds reduce productivity in the remaining patches by ≤41%. The additional future management scenarios we assessed resulted in population declines. Synthesis and applications. Our study indicates that the Kirtland's warbler population is stable under current management conditions and that the jack pine plantation and cowbird removal programmes continue to be necessary for the long-term persistence of the species. This study represents one of the first attempts to incorporate full annual cycle dynamics into a population viability analysis for a migratory bird, and our results indicate that incorporating wintering grounds dynamics improved the model performance.
Data from: Developing state and transition models of floodplain vegetation dynamics as a tool for conservation decision-making: a case study of the Macquarie Marshes Ramsar wetland
1. Floodplain vegetation states (communities) exhibit spatiotemporal dynamics in vegetation structure and composition, which reflect unique hydrological and connectivity patterns. Shifts in inundation regimes can drive succession and establish new stable states, determined by the magnitude and duration of the hydrological perturbation. 2. We aimed to develop a modelling approach that is able to capture ecosystem dynamics, identify and quantify the main drivers of change, and provide a tool for conservation decision-making. We developed state and transition models for floodplain vegetation states based on surveys in 1991 and 2008 in the Macquarie Marshes (Australia), a Ramsar wetland of international importance. We used a Bayesian logistic regression approach to model state and transitions between vegetation states and investigated how flood frequency, distance to stream and fire frequency were associated with vegetation dynamics during this period. 3. During 1991–2008, significant transitions have occurred towards drier states. Semi-permanent wetland vegetation had the lowest persistence probability (ppsis = 0·456) and a significant threshold response of transitioning to terrestrial vegetation (ptran = 0·505). Transition to drier states was driven by lower inundation probabilities followed by increased fire probability, and distance to nearest stream. 4. Using developed models, we predicted persistence probabilities of vegetation states under an unregulated (i.e. no dams or diversions) and regulated water availability system. Under a regulated system, semi-permanent wetland vegetation had an average persistence of ppsis = 0. 67 and 0·08 in the northern and southern sections of the nature reserve, respectively. Under an unregulated system, the predicted persistence of semi-permanent wetland vegetation was considerably higher: ppsis = 0·87 and 0·38, respectively. 5. Synthesis and applications. Developing quantitative models of state transitions significantly improved our understanding of ecosystem dynamics, identifying sensitive indicators for monitoring and thus supporting conservation decision-making. This helps managers understand potential trajectories of change in ecosystems in response to management options. For example, increasing environmental flows in the Macquarie Marshes is predicted to shift the community towards more of a wetland than the terrestrial state, resulting from river regulation. State and transition models identified how key ecological assets respond to drivers of change, particularly where these can be managed. This is critical for ensuring that all ecosystem components are managed and that these do not shift into undesirable states.
Spatially explicit models for decision-making in animal conservation and restoration
<p>Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79 %), towards the species and population level (80 %) and towards conservation (rather than restoration) applications (71 %). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10 % of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration, and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by (1) developing a toolbox with multiple, easier-to-use methods, (2) improving calibration and validation of dynamic modelling approaches, and (3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by (4) combining multiple modelling approaches to assess uncertainty, and (5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.</p>
Data for: Habitat functionality: integrating environmental and geographic space in niche modelling for conservation planning
<p>Niche modelling is typically used to assess the effects of anthropogenic land use and climate change on species distributions and to inform spatial conservation planning. These models focus on the suitability of local biotic and abiotic conditions for a species in environmental space (E-space). Although movements also affect species occurrence, efforts to formally integrate geographic space (G-space) into niche modelling have been hindered by the lack of comprehensive theoretical frameworks. </p> <p>We propose the 'functional habitat' framework to define areas that are simultaneously of high-quality in E-space and functionally connected to other suitable habitat in G-space. Originating in metapopulation ecology, approaches have been developed to assess the amount of suitable connected habitat, based on the proximity between pairs of locations. Using network theory, which operates in topological space (T-space, defined by a network), we extended these metapopulation approaches to integrate movement constraints in G-space with niche modelling in E-space. </p> <p>We demonstrate the functional habitat framework using empirical data (GPS-tracking and population monitoring) throughout the European wild mountain reindeer (<em>Rangifer t. tarandus</em>) distribution range. We show that functional habitat outperforms traditional suitability in explaining the species' distribution. This approach integrates effects from habitat loss and fragmentation for spatial conservation planning and avoids overemphasizing small, inaccessible areas with locally suitable habitat. The functional habitat framework formally integrates biotic, abiotic, and movement constraints in niche modeling using network theory, thus opening a wide range of applications in spatial conservation planning.</p>
Multi-scale Modeling of Breast Conserving Therapy
ClinicalTrials.gov study NCT02310711. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Spatio-temporal modelling of auk abundance after the Erika oil spill and implications for conservation
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Data from: Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea
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Data from: A generalizable energetics-based model of avian migration to facilitate continental-scale waterbird conservation
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Data from: Conservation versus livelihoods: spatial management of non-timber forest product harvests in a two-dimensional model
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Data from: Development and validation of an expert-based habitat suitability model to support boreal caribou conservation
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.